Enterprises are looking at ways of reducing costs and increasing efficiencies of their data processing system. A typical enterprise data processing system allocates individual resources for each of the enterprise's applications. Enough resources are acquired for each application to handle the estimated peak load of the application. Each application has different load characteristics; some applications are busy during the day; some others during the night; some reports are run once a week and some others once a month. As a result, there is a lot of resource capacity that is left unutilized. Grid computing enables the utilization or elimination of this unutilized capacity. In fact, grid computing is poised to drastically change the economics of computing.
A grid is a collection of computing elements that provide processing and some degree of shared storage; the resources of a grid are allocated dynamically to meet the computational needs and priorities of its clients. Grid computing can dramatically lower the cost of computing, extend the availability of computing resources, and deliver higher productivity and higher quality. The basic idea of grid computing is the notion of computing as a utility, analogous to the electric power grid or the telephone network. A client of the grid does not care where its data is or where the computation is performed. All a client wants is to have computation done and have the information delivered to the client when it wants.
This is analogous to the way electric utilities work; a customer does not know where the generator is, or how the electric grid is wired. The customer just asks for electricity and gets it. The goal is to make computing a utility—a ubiquitous commodity. Hence it has the name, the grid.
This view of grid computing as a utility is, of course, a client side view. From the server side, or behind the scenes, the grid is about resource allocation, information sharing, and high availability. Resource allocation ensures that all those that need or request resources are getting what they need. Resources are not standing idle while requests are left unserviced. Information sharing makes sure that the information clients and applications need is available where and when it is needed. High availability ensures that all the data and computation must always be there—just as a utility company must always provide electric power.
Grid Computing for Databases
One area of computer technology that can benefit from grid computing is database technology. A grid can support multiple databases and dynamically allocate and reallocate resources as needed to support the current demand for each database. As the demand for a database increases, more resources are allocated for that database, while other resources are deallocated from another database. For example, on an enterprise grid, a database is being serviced by one database server running on one server blade on the grid. The number of users requesting data from the database increases. In response to this increase in the demand for the database, a database server for another database is removed from one server blade and a database server for the database experiencing increased user requests is provisioned to the server blade.
Grid computing for databases can require allocation and management of resources at different levels. At a level corresponding to a single database, the performance and resource availability provided to the users of the database must be monitored and resources of the database allocated between the users to ensure performance and resource availability goals for each of the users are met. Between databases, the allocation of a grid's resources must be managed to ensure that performance and resource availability goals for users of all the databases are met.
A mechanism that can allocate resources at these different levels is described in Hierarchical Management of the Dynamic Allocation of Resources in a Multi-Node System Ser. No. 10/917,873. One measure employed by this mechanism to manage allocation of resources used for a database is referred to as session balancing. Session balancing is used to balance workload between the servers of a multi-node server that manages access to a particular database. Each database server that comprises the multi-node server is referred to herein as a database instance. Session balancing entails balancing a number of sessions between database instances to optimize performance realized by a user and ensures requirements for performance are being met.
Two forms of session balancing that may be used are connection-time session balancing and run-time session balancing. Under run-time session balancing, sessions on one database instance, where workload may be too high, are moved to a target database instance that offers relatively superior performance. Under connection-time balancing, a user requesting a connection to a multi-node database server is connected to a database instance that provides better performance for the user.
In both types of session balancing, a database instance is selected based on whether the database instance can provide relatively superior performance for a user. Therefore, there is a need to measure and determine which server from a set of servers can provide relatively superior performance for a user.
Approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.